R for Assessment Specialists

The aim of this workshop is to teach assessment practitioners and researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain. The lessons below were designed for those interested in working with assessment data in R. Some of the content is adapted/remixed from the Data Carpentry R for Social Scientists lessons https://datacarpentry.org/r-socialsci/.

This is an introduction to R designed for participants with no programming experience. These lessons can be taught in about two days. They start with some basic information about R syntax, the RStudio interface, and move through how to import CSV files, the structure of data frames, how to deal with factors, how to add/remove rows and columns, how to calculate summary statistics from a data frame, and a brief introduction to plotting. They also include information about how to carry out a variety of analytical techniques specific to the field of assessment (e.g., classical test theory, criterion-referenced test theory, and equating)

Getting Started

This workshop is hands-on, so participants are encouraged to use their own computers to ensure the proper setup of tools for an efficient workflow.

These lessons assume no prior knowledge of the skills or tools.

To get started, follow the directions in the “Setup” tab to install R and RStudio.

Prerequisites

This lesson requires a working copy of R and RStudio.
To most effectively use these materials, please make sure to install everything before working through this lesson.

For Workshop Leaders

If you are teaching this lesson in a workshop, please see the Instructor notes.

Schedule

	Setup	Download files required for the lesson
00:00	1. Before we Start	How to find your way around RStudio? How to interact with R? How to manage your environment? How to install packages?
00:25	2. Part 1: Introduction to R	What data types are available in R? What is an object? How can values be initially assigned to variables of different data types? What arithmetic and logical operators can be used? How can subsets be extracted from vectors and data frames? How does R treat missing values? How can we deal with missing values in R?
00:50	3. Part 1: Starting with Data	What is a data.frame? How can I read a complete csv file into R? How can I get basic summary information about my dataset? How can I change the way R treats strings in my dataset? Why would I want strings to be treated differently? How are dates represented in R and how can I change the format?
01:50	4. Part 1: Introducing dplyr and tidyr	How can I select specific rows and/or columns from a data frame? How can I combine multiple commands into a single command? How can create new columns or remove existing columns from a data frame? How can I reformat a dataframe to meet my needs?
02:55	5. Part 1: Data visualisation with ggplot2	What are the components of a ggplot? How do I create scatterplots, boxplots, and barplots? How can I change the aesthetics (ex. colour, transparency) of my plot? How can I create multiple plots at once?
04:10	6. Part 2: CTT and CRT Test and Item Analysis	How do I conduct basic CTT/CRT item analyses? How do I investigate the reliability/dependability of a test? How do I extract indices of interest for reporting and analysis?
05:05	7. Part 3: Office Hours	Can I apply anything I’ve learned today to new data?
06:40	8. Part 4: Test Equating	How do I prepare data for test equating? How do I conduct test equating? How do I extract indices of interest for comparison, reporting, and analysis? how do I visualize equated relationships?
07:30	9. Part 4: Item Respose Modeling	Can I do item response modeling in R?
09:05	10. Part 4: Advanced Analyses	How can I carry out confirmatory factor analysis? How can I compare two models?
10:40	11. Part 5: Reproducible Reporting	Can I use Rmarkdown to create research reports How do I automate score reporting? How do I combine my analysis and reporting?
11:55	12. Part 6: Office Hours Part Two	Can I apply anything I’ve learned today to new data?
13:30	Finish

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.